Parsing resume using NER and NLP
Recruiters and HR teams in companies have a tough time scanning thousands of qualified resumes. Either they need many people to do this or they miss out on qualified candidates. This is a waste of time, money and productivity for the company.
To solve this, our resume parser application can take in millions of resumes, parse the needed fields and categorise them. This resume parser uses the popular python library - Spacy for OCR and text classifications. First we train our model with these fields, then the application can pick out the values of these fields from new resumes being input.
The dataset of resumes has the following fields:
1.Location
2.Designation
3.Name
4.Years of Experience
5.College
6.Degree
7.Graduation Year
8.Companies worked at
9.Email address
I have used spacy on this project. To know more about spacy follow the link Spacy